NDLI: Combining categorical and continuous spatial information within the Bayesian maximum entropy paradigm

Content Provider	Springer Nature Link
Author	Wibrin, M.A. Bogaert, P. Fasbender, D.
Copyright Year	2006
Abstract	Due to the fast pace increasing availability and diversity of information sources in environmental sciences, there is a real need of sound statistical mapping techniques for using them jointly inside a unique theoretical framework. As these information sources may vary both with respect to their nature (continuous vs. categorical or qualitative), their spatial density as well as their intrinsic quality (soft vs. hard data), the design of such techniques is a challenging issue. In this paper, an efficient method for combining spatially non-exhaustive categorical and continuous data in a mapping context is proposed, based on the Bayesian maximum entropy paradigm. This approach relies first on the definition of a mixed random field, that can account for a stochastic link between categorical and continuous random fields through the use of a cross-covariance function. When incorporating general knowledge about the first- and second-order moments of these fields, it is shown that, under mild hypotheses, their joint distribution can be expressed as a mixture of conditional Gaussian prior distributions, with parameters estimation that can be obtained from entropy maximization. A posterior distribution that incorporates the various (soft or hard) continuous and categorical data at hand can then be obtained by a straightforward conditionalization step. The use and potential of the method is illustrated by the way of a simulated case study. A comparison with few common geostatistical methods in some limit cases also emphasizes their similarities and differences, both from the theoretical and practical viewpoints. As expected, adding categorical information may significantly improve the spatial prediction of a continuous variable, making this approach powerful and very promising.
Starting Page	423
Ending Page	433
Page Count	11
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	20
Issue Number	6
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-02-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian maximum entropy Geostatistics Kriging Random field Categorical data Soft data Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Math. Application in Environmental Science Numerical and Computational Methods in Engineering Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Probability Theory and Stochastic Processes Math. Applications in Geosciences
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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